A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series
This addresses the need for more precise relationship detection in time series analysis, particularly in domains like climate science, but it appears incremental as it builds on traditional approaches by focusing on sub-intervals.
The paper tackles the problem of identifying relationships that occur only in small sub-intervals of time series, rather than across entire series, by proposing a fast-optimal guaranteed algorithm to find the most interesting sub-interval relationships, and demonstrates its utility with a real-world climate science dataset, showing scalability and domain insights.
Traditional approaches focus on finding relationships between two entire time series, however, many interesting relationships exist in small sub-intervals of time and remain feeble during other sub-intervals. We define the notion of a sub-interval relationship (SIR) to capture such interactions that are prominent only in certain sub-intervals of time. To that end, we propose a fast-optimal guaranteed algorithm to find most interesting SIR relationship in a pair of time series. Lastly, we demonstrate the utility of our method in climate science domain based on a real-world dataset along with its scalability scope and obtain useful domain insights.